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Multi-swarm fruit fly optimization algorithm for structural damage identification

  • Li, S. (Department of Applied Mechanics, Sun Yat-sen University) ;
  • Lu, Z.R. (Department of Applied Mechanics, Sun Yat-sen University)
  • Received : 2015.04.04
  • Accepted : 2015.10.20
  • Published : 2015.11.10

Abstract

In this paper, the Multi-Swarm Fruit Fly Optimization Algorithm (MFOA) is presented for structural damage identification using the first several natural frequencies and mode shapes. We assume damage only leads to the decrease of element stiffness. The differences on natural frequencies and mode shapes of damaged and intact state of a structure are used to establish the objective function, which transforms a damage identification problem into an optimization problem. The effectiveness and accuracy of MFOA are demonstrated by three different structures. Numerical results show that the MFOA has a better capacity for structural damage identification than the original Fruit Fly Optimization Algorithm (FOA) does.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China, Ministry Education China, Guangdong Province Natural Science Foundation

References

  1. Abolbashari, M.H., Nazari, F. and Rad, J.S. (2014), "A multi-crack effects analysis and crack identification in functionally graded beams using particle swarm optimization algorithm and artificial neural network", Struct. Eng. Mech., 51(2), 299-313. https://doi.org/10.12989/sem.2014.51.2.299
  2. Begambrea, O. and Laier, J.E. (2009), "Laiera a hybrid particle swarm optimization-simplex algorithm (PSOS) for structural damage identification", Adv. Eng. Softw., 40(9), 883-891. https://doi.org/10.1016/j.advengsoft.2009.01.004
  3. Charalampakis, A.E and Dimou, C.K. (2010), "Identification of Bouc-Wen hystertic systems using particle swarm optimization", Comput. Struct., 88(21-22), 1197-1205. https://doi.org/10.1016/j.compstruc.2010.06.009
  4. Chou, J.H. and Ghaboussi, J. (2001), "Genetic algorithm in structural damage detection", Comput. Struct., 79(14), 1335-1353. https://doi.org/10.1016/S0045-7949(01)00027-X
  5. Dackermann, U., Smith, W.A. and Randall R.B. (2014), "Damage identification based on response-only Measurements using cepstrum analysis and artificial neural network", Struct. Health Monit., 13(4), 430-444. https://doi.org/10.1177/1475921714542890
  6. Fan, W. and Qiao, P.Z. (2011), "Vibration-based Damage Identification Method: A Review and Comparative Study", Struct. Health Monit., 10(1), 83-110. https://doi.org/10.1177/1475921710365419
  7. Friswell, M.I., Penny, J.E.T. and Garvey, S.D. (1998), "A combined genetic and eigensensitivity algorithm for the location of damage in structures", Comput. Struct., 69(5), 547-556. https://doi.org/10.1016/S0045-7949(98)00125-4
  8. Guo, H.Y. and Li, Z.L. (2014), "Structural damage identification based on evidence fusion and improved particle swarm optimization", J. Vib. Control, 20(9), 1279-1292. https://doi.org/10.1177/1077546312469422
  9. Kang, F., Li, J.J. and Xu, Q. (2012), "Damage detection based on improved particle swarm optimization using vibration data", Appl. Soft Comput., 12(8), 2329-2335. https://doi.org/10.1016/j.asoc.2012.03.050
  10. Kaveh, A. and Zolghadr, A. (2015), "An improved CSS for damage detection of truss structures using changes in natural frequencies and mode shapes", Adv. Eng. Softw., 80, 93-100. https://doi.org/10.1016/j.advengsoft.2014.09.010
  11. Kwon, Y.D., Kwon, H.W., Kim, W. and Yeo, S.D. (2008), "Structural damage detection in continuum structures using successive zooming genetic algorithm", Struct. Eng. Mech., 30(2), 135-146. https://doi.org/10.12989/sem.2008.30.2.135
  12. Li, J.Q., Pan, Q.K., Mao, K. and Suganthan, P.N. (2014), "Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm", Knowled. Bas. Syst., 72, 28-36. https://doi.org/10.1016/j.knosys.2014.08.022
  13. Maresa, C. and Suraceb, C. (1996), "An application of genetic algorithm to identify damage in elastic structures", J. Sound Vib., 195(2), 195-215. https://doi.org/10.1006/jsvi.1996.0416
  14. Majumdar, A., De, A., Maity, D. and Maiti, D.K. (2013), "Damage assessment of beams from changes in natural frequencies using ant colony optimization", Struct. Eng. Mech., 45(3), 391-410. https://doi.org/10.12989/sem.2013.45.3.391
  15. Mohan, S.C., Yadav, A., Maiti, K.D. and Maity, D. (2014), "A comparative study on crack identification of structures from the changes in natural frequencies using GA and PSO", Eng. Comput., 31(7), 1514-1531. https://doi.org/10.1108/EC-02-2013-0061
  16. Pan, W.T. (2011), Fruit Fly Optimization Algorithm, Tsang Hai Book Publishing, Taiwan.
  17. Pan, W.T. (2012), "A new fruit fly optimization algorithm: taking the financial distress model as an example", Knowled. Bas. Syst., 26, 69-74. https://doi.org/10.1016/j.knosys.2011.07.001
  18. Shiraz, M.R.N., Mollamahmoudi, H. and Seyedpoor, S.M. (2014), "Structural damage identification using an adaptive multi-stage optimization method based on a modified particle swarm algorithm", J. Optim. Theor. Appl., 160, 1009-1019. https://doi.org/10.1007/s10957-013-0316-6
  19. Tsou, P. and Shen, H.H.H. (1994), "Structural damage detection and identification using neural networks", AIAA J., 32(1), 176-183. https://doi.org/10.2514/3.11964
  20. Yi, T.H., Zhou, G.D., Li, H.N. and Zhang, X.D. (2015), "Optimal sensor placement for health monitoring of high-rise structure based on collaborative-climb monkey algorithm", Struct. Eng. Mech., 54(2), 305-317. https://doi.org/10.12989/sem.2015.54.2.305
  21. Yuan, X.F., Dai, X.S., Zhao, J.Y. and He, Q. (2014), "On a novel multi-swarm fruit fly optimazition algorithm and its application", Appl. Math. Comput., 233, 260-271. https://doi.org/10.1016/j.amc.2014.02.005

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